A Prediction Algorithm for Paddy Leaf Chlorophyll Using Colour Model Incorporate Multiple Linear Regression

Thaiparnit, Sattarpoom; Ketcham, Mahasak

doi:10.4186/ej.2017.21.3.269

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…All ground-truth data are available through the Open Science Framework in the Supplementary Materials section. On the other hand, Figure 3a shows the linear correlation used to relate the measured SPAD value with the leaf-blade N concentration [37].…”

Section: Rice Cropsmentioning

confidence: 99%

“…As detailed in Table 2, the ground-truth for training machine learning (ML) algorithms was defined based on the direct measurements of plant chlorophyll using a SPAD 502 Plus meter (Konica-Minolta) over these sampled areas, as depicted in Figure 1. Datasets contain the measured SPAD value that directly correlates with the leaf-blade N concentrations by following the linear correlation [37] defined in Figure 3a. In this regard, measurements from the crop were obtained during three stages of rice growth: vegetative, reproductive, and ripening, in which 3 trials were conducted per crop stage.…”

Section: Machine Learning For N Estimationsmentioning

confidence: 99%

“…Table 2) containing the direct measurements of leaf chlorophyll based on SPAD readings. In this regard, the estimation results are all given in SPAD scale, in which the linear relationship with nitrogen is defined as: N = 0.079(SPAD) − 0.154, according to the work reported in [37].…”

Section: Nitrogen Estimationsmentioning

confidence: 99%

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Estimation of Nitrogen in Rice Crops from UAV-Captured Images

et al. 2020

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Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks(NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections.

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Section: Rice Cropsmentioning

confidence: 99%

Section: Machine Learning For N Estimationsmentioning

confidence: 99%

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Estimation of Nitrogen in Rice Crops from UAV-Captured Images

et al. 2020

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“…Numbers of plants or fruits would be the crop yields which was led from the quality of seeds. Properties of plants and leaves could refer to crop health and problem which reflex to crop yield which could be an automatic detection based on image analysis [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Automatic Assessment of Seed Germination Percentage

Chaivivatrakul¹

2020

EngJ

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This research was designed to investigate an automatic seed germination rate for the top of paper germination method. Chili and guinea were adopted to be used in the experiment with a 4-time repetition and 2 sets of the germination group (4-separated plates with 50 seeds per plate, 2 sets per seed type, totally 400 seeds of chili and 400 seeds of quinea). Two detection methods were proposed binary thresholding and maximum likelihood; based on color analysis. An uncontrolled environment image taking was the way to collect image data. The results were compared to a hand-labeling groundtruth. Both methods achieved accuracy rate higher than 93% which was promising to implement this system. The binary thresholding was a lightweight method suitable for a very limited resource software environment system. The maximum likelihood was more complex. The method had more potential than the binary thresholding, it was flexible to the light condition, returned few false alarms per image (less than 3 false alarms per image). Maximum likelihood could be adopted to implement in a proper environment which still could be in a mobile device.

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